In today's
demand-dynamic economy, the Australian process industry needs to shift from traditional
mass production to smart manufacturing for more agile, cost-effective and
flexible process operation responding to the market. While governments and
industries worldwide have heavy invested in this new industry paradigm,
developments are largely limited to its information technology aspect. This
project will investigate the process control methodologies crucial to smart
manufacturing.

Based on
contraction and dissipativity theories, this project aims to develop a
distributed optimization-based nonlinear control approach for plantwide
flexible manufacturing, which can achieve time-varying operational targets
including production rates and product specifications to meet dynamic market
demands. This includes a contraction-based nonlinear distributed control
framework that ensures plantwide stability at any feasible setpoints or
references and a distributed economic model predictive control approach that
coordinates autonomous controllers to achieve plantwide economic objectives in
a self-organizing manner. The outcomes of this project are expected to form a
process control framework for next-generation smart plants.

This project aims to develop a novel alumina feeder design and an
advanced real-time cell control strategy to achieve more uniform and smooth
alumina concentration spatially and temporally, more uniform anode current
distribution, and better distributed heat management, resulting in a more
balanced and stable cell with reduced background perfluorocarbon emission and
sludge formation.

Modern industrial processes are very complex, with distributed process
units via a network of material and energy streams. Their operations
increasingly depend on automatic control systems, which can make the plants
susceptible to faults such as sensor/actuator failures. Occurrence of faults is
increased by the common practice to operate processes close to their design
constraints for economic considerations. This project will develop a new
approach to detect and reduce the impact of these faults, which can cause
significant economic, environment and safety problems.

Based on the concept of dissipative systems, this project aims to develop
a novel integrated approach to distributed fault diagnosis and fault-tolerant
control for plantwide processes. The key dynamic features of normal and
abnormal processes are captured by their dissipativity properties, which are
used to develop an efficient online fault diagnosis approach based on process
input and output trajectories, without the use of state estimators or residual
generators. Using the dissipativity framework, a distributed fault diagnosis
approach will be developed to identify the locations and faults in a process
network. A distributed fault tolerant control approach will be developed to
ensure plantwide stability and performance.

The ever-increasing integration of distributed renewable energy
generation sources with the electricity grid reduces our reliance on fossil
fuels and carbon emissions but also presents risks to the grids stable and
reliable operation due to intermittent nature of such sources. This project
will develop some key technologies of battery energy storage and control to
address the above issues and help defer the investment for the augmentation of
the transmission and distribution networks.

This project aims to develop a new control approach to distributed energy
storage at stack, system and microgrid levels, utilising one of the most
promising flow battery technologies - Vanadium Redox batteries. This is the
first attempt of a storage centric approach that includes (1) an integrated
approach to design and control of Vanadium flow batteries with novel advanced power
electronics technologies to achieve optimal charging/discharging conditions and
(2) a scalable distributed energy storage and power management approach
incorporating energy pricing for storage dispatch that allows distributed
autonomous controllers to achieve optimal local techno-economic performance and
microgrid-wide efficiency and reliability.

Based on the behavioural approach
to systems and dissipativity theory, this project aims to integrate nonlinear
control theory with distributed optimization to develop a novel distributed
predictive control approach for complex industrial processes. In this approach,
the global objectives (i.e., the plantwide stability and performance) are
converted into the local constraints of dissipativity conditions for
non-cooperative optimization performed in the distributed controllers. The
outcomes will include a framework and the fundamental control theory for
distributed autonomous model predictive control that achieves improved
scalability, flexibility and robustness compared with existing distributed
predictive control approaches.

This project
develops a new monitoring approach for monitoring aluminium smelting cells,
including an instrumentation scheme for measuring distributed process variables
and a soft sensor method for estimating the important process variables that
cannot be directly measured.

Fouling reduces
throughput and productivity of membrane systems and as such increases operating
costs and reduces profitability of water treatment industries. This work aims
to reduce membrane fouling by reducing the amount of solute at the membrane
surface. This is achieved by implementing destabilizing electro-osmotic flow
control. The significance of this project lies in linking feedback control of
electro-osmotic effects with spacer design to maximize flow instabilities. This
project will advance modelling of flow in membrane channels and develop a novel
feedback flow control strategy that enhances mixing. The effectiveness and
operability of the new fouling reduction approach on real-world membrane
systems will be evaluated. With over $9bn worth of membrane-based desalination
plants either in operation, under construction or being planned in Australia,
the expected outcomes of this project will lead to significant social and economical benefit and provide greater water security.

The objective of the
proposed project is to develop an online dynamic feedback control approach to
improve the operation of paste thickeners through adopting modern control
strategies (in particular, model predictive control)
already successfully applied in the petro-chemical industry. This would be an
ideal test case for applying advanced dynamic control for complete CHPPs or
other variable dynamic processes such as flotation.

§Plantwide Control of Modern Chemical Processes from a
Network Perspective (ARC Discovery Project: DP1093045, 2010-2014, $280K)

To achieve high economical
efficiency, modern chemical plants are becoming increasingly complex, to an extent
that cannot be effectively managed by existing process modelling and control
techniques. By exploring the physical fundamentals in thermodynamics and their
connections to control theory, this project aims to develop a new modelling and
control approach that can be applied to complicated nonlinear processes. In
this approach, processes over the entire plant are analysed and controlled from
a network perspective using the dissipativity control theory. The outcomes of
this project will form the cornerstones of a new process control paradigm that
offers more robust and reliable process operation at any scale.

Primary production of aluminium is highly energy intensive,
with energy costs representing 22-36% of operating costs in smelters. The
Australian aluminium smelting industry consumed 29,500 GWh of electricity in
2007, 13% of final electricity consumption in Australia. The long
term sustainability of the aluminium smelting industry depends on
energy-efficient production technologies for global competitiveness. The aim of
the project is to improve auto-diagnosis of the occurrence of the root-cause
for abnormal process conditions in the smelting cells that adversely impact
energy and environmental efficiencies. The expected outcomes include: (1) An
adaptive model for the change in control signal and control algorithms with
different abnormalities and at different operating line current levels, (2) A
sequence of diagnostic sub-routines based on processing signals at different,
(3) A schemes for alarms and guidelines for human interface interaction when
needed.

53.Tang A., Bao
J. and Skyllas-Kazacos M.* (2011) Dynamic Modelling of the Effects of Ion
Diffusion and Side Reactions on the Capacity Loss for Vanadium Redox Flow
Battery. Journal of Power Sources196,
10737 10747.

3.Wang
R.G. and Bao J.* (2017)
Advanced-step Nonlinear Model Predictive Control Based on Contraction Analysis.
Proc. 20th World Congress of the
International Federation of Automatic Control, Toulouse, pp9401-9406.

4.Zheng
C.X. and Bao J.* (2017) Robust
Distributed Control for Plantwide Processes Based on Dissipativity in Quadratic
Differential Forms. Proc. 6th
International Symposium on Advanced Control of Industrial Processes,
Taipei, pp7-12.

9.Wang
R.G. and Bao J.* (2016) Asymptotic
Tracking of Periodic Operation Based on Control Contraction Metrics. Proc. 11th
IFAC International Symposium on Dynamics and Control of Process Systems,
Trondheim, pp574-578.

55.Tippett M.J. and Bao J.* (2012) Distributed Control of Large-Scale Systems based on
Dissipativity with Quadratic Differential Forms. Proc. 20th International Symposium on the Mathematical Theory of
Networks and Systems, Melbourne: Paper 0186.

73.Tran T. and Bao J.* (2009) A Real-Time Trajectory-Based Stability Constraint
for Model Predictive Control. Proc. 7th
IEEE International Conference on Control and Automation, New Zealand,
pp2094-2099, New Zealand, December 9-11, 2009.

74.Setiawan R., Bao J.*, Yee, K.W.K. (2009) Operability Analysis of a
Multiple-stage Membrane Process based on Network Approach. Proc. 7th IEEE International Conference on Control and Automation,
pp2054-2059, New Zealand, December 9-11, 2009

75.Xu S.C. and Bao J.* (2009) Multirate Networked
Control of Plantwide Chemical Processes. Proc.
7th IEEE International Conference on Control and Automation, pp942-947, New
Zealand, December 9-11, 2009.

99.Bao
J.*, Zhang W.Z. and Lee
P.L. (2002) A New Paring Method for Multi-loop Control Based on the Passivity
Theorem. Proc. International Symposium on
Advanced Control of Industrial Processes, Kumamoto: 545-550.